1. Field of the Invention
The present invention relates to signal detection and, more particularly, to a method for detecting micro-calcifications in mammograms using novel algorithms and stochastic resonance noise.
2. Description of the Related Art
Stochastic resonance (SR) is a nonlinear physical phenomenon in which the output signals of some nonlinear systems can be enhanced by adding suitable noise under certain conditions. The classic SR signature is the signal-to-noise ratio (SNR) gain of certain nonlinear systems, i.e., the output SNR is higher than the input SNR when an appropriate amount of noise is added.
Although SNR is a very important measure of system performance, SNR gain-based SR approaches have several limitations. First, the definition of SNR is not uniform and it varies from one application to another. Second, to optimize the performance, the complete a priori knowledge of the signal is required. Finally, for detection problems where the noise is non-Gaussian, SNR is not always directly related to detection performance; i.e., optimizing output SNR does not guarantee optimizing probability of detection.
In signal detection theory, SR also plays a very important role in improving the signal detectability. For example, improvement of detection performance of a weak sinusoid signal has been reported. To detect a DC signal in a Gaussian mixture noise background, performance of the sign detector can be enhanced by adding some white Gaussian noise under certain circumstances. For the suboptimal detector known as the locally optimal detector (LOD), detection performance is optimum when the noise parameters and detector parameters are matched. The stochastic resonance phenomenon in quantizers results in a better detection performance can be achieved by a proper choice of the quantizer thresholds. Detection performance can be further improved by using an optimal detector on the output signal. Despite the progress achieved by the above approaches, the use the SR effect in signal detection systems is rather limited and does not fully consider the underlying theory of SR.
Simple and robust suboptimal detectors are used in numerous applications. To improve a suboptimal detector detection performance, two approaches are widely used. In the first approach, the detector parameters are varied. Alternatively, when the detector itself cannot be altered or the optimum parameter values are difficult to obtain, adjusting the observed data becomes a viable approach. Adding a dependent noise is not always possible because pertinent prior information is usually not available.
For some suboptimal detectors, detection performance can be improved by adding an independent noise to the data under certain conditions. For a given type of SR noise, the optimal amount of noise can be determined that maximizes the detection performance for a given suboptimal detector. However, despite the progress made, the underlying mechanism of the SR phenomenon as it relates to detection problems has not fully been explored. For example, until now the “best” noise to be added in order to achieve the best achievable detection performance for the suboptimal detector was not known. Additionally, the optimal level of noise that should be used for enhanced performance was also unknown.
Breast cancer is a serious disease with high occurrence rate in women. There is clear evidence which shows that early diagnosis and treatment of breast cancer can significantly increase the chance of survival for patients. One of the important early symptoms of breast cancer in the mammograms is the appearance of micro-calcification clusters. An accurate detection of micro-calcifications is highly desirable to ensure early diagnosis of breast cancer.
Automatic micro-calcification detection techniques play an important role in cancer diagnosis and treatment. This, however, still remains a challenging task.
For example, computer-aided diagnosis (CAD) improves the diagnostic performance of radiologists and is an effective method for early diagnosis thereby increasing survival time for women with breast cancer. While advances have been made in the area of CAD for digital mammograms, the main challenge of accurately identifying breast cancer in digital mammograms still remains, which is due to the small sizes and subtle contrast of the lesions compared with the surrounding normal breast tissues.
Much effort has been made for detecting micro-calcifications by using CAD techniques. Some methods tried to detect micro-calcifications through a modeling procedure. For example, Bazzani et al. and Gurcan et al. detected the micro-calcifications by using Gaussianity tests in the difference and filtered mammograms, respectively. See Armando Bazzani et al., “Automatic detection of clustered micro-calcifications in digital mammograms using an SVM classifier,” in Proc. of European Symposium on Artificial Neural Networks Plastics, Bruges, 26-28, April, 2000; M. Nafi Gurcan, Yasemin Yardimci, and A. Enis Getin, “Influence function based gaussianity tests for detection of micro-calcifications in mammogram images,” in Proc. International Conference on Image Processing (ICIP), vol. 3, pp. 407-411, 1999. Karssemeijer modeled the mammograms using Markov random fields. See N. Karssemeijer, “Adaptive noise equalization and recognition of micro-calcification clusters in mammograms,” Int. J. Pattern Recognit. Artificial Intell., vol. 7, no. 6, pp. 1357-1376, 1993. Nakayama et al. used a Gaussian probability density function (PDF) to model the abnormal regions in the subband mammograms generated by a novel filter bank. See Ryohei Nakayama et al, “Computer-aided diagnosis scheme using a filter bank for detection of micro-calcification clusters in mammograms,” IEEE Trans. on Biomedical Engineering, vol. 53, no. 2, pp. 273-283, February 2006. Regentova et al. considered the PDFs of the magnitudes of the wavelet coefficients, which are assumed to correspond to two hidden Markov states, to obey zero mean Gaussian distributions with different variances. See Emma Regentova et al, “Detecting micro-calcifications in digital mammograms using wavelet domain hidden markov tree model,” in Proc. 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2006 (EMBS '06), pp. 1972-1975, 30, Aug.-3, September 2006.
Deepa and Tessamma used the deterministic fractal model to characterize breast background tissues. See Sankar Deepa and Thomas Tessamma, “Fractal modeling of mammograms based on mean and variance for the detection of micro-calcifications,” in Proc. International Conference on Computational Intelligence and Multimedia Applications, vol. 2, pp. 334-348, 13-15, December, 2007. The challenge for these model-based methods is that an accurate model is generally not easy to obtain and model mismatch is hard to avoid, so the detection results are deteriorated. There are also some methods that attempt to avoid the necessity of modeling during the detection process. For example, in Wei et al., relevance vector machine (RVM) was employed as a micro-calcification classifier, and its parameters were determined through a supervised learning procedure. See Liyang Wei et al., “Relevance vector machine for automatic detection of clustered micro-calcifications,” IEEE Trans. on Medical Imaging, vol. 24, no. 10, pp. 1278-1285, October 2005. Catanzariti et al. trained a three-layer feed-forward artificial neural network (ANN) to detect micro-calcifications using the features extracted by a bank of Gabor filters. See Catanzariti et al, “A CAD system for the detection of mammographyc micro-calcifications based on Gabor Transform,” in Proc. Nuclear Science Symposium Conference Record, vol. 6, pp. 3599-3603, 16-22 Oct. 2004.
Strickland et al., Lemaur et al. and Li and Dong proposed the wavelet domain thresholding techniques to obtain the information of interest for the detection of micro-calcifications. See R. N. Strickland, “Wavelet transform methods for objects detection and recovery,” IEEE Trans. Image Processing, vol. 6, pp. 724-735, May, 1997; G. Lemaur, K. Drouiche, and J. DeConinck, “Highly regular wavelets for the detection of clustered micro-calcifications in mammograms,” IEEE Trans. on Medical Imaging, vol. 22, no. 3, March, 2003; Kai-yang Li and Zheng Dong, “A novel method of detecting calcifications from mammogram images based on wavelet and sobel detector,” in Proc. 2006 IEEE International Conference on Mechatronics and Automation, pp. 1503-1508, June 2006. These methods partially bypassed the modeling problems, but determination of the optimum parameters, such as the threshold, is still a very challenging task, and the detection performance was often affected by the suboptimum parameters. Basically, lesion detection can be considered as an anomaly detection problem. Performance of the detectors is heavily dependent on the accuracy of the mathematical models and the detector parameters. However, appropriate models and optimum parameter values are generally very difficult to obtain in practical applications, which often results in unsatisfactory detection performance in terms of high probability of false alarm (PF) and low probability of detection (PD).
Description of the Related Art Section Disclaimer: To the extent that specific publications are discussed above in this Description of the Related Art Section, or elsewhere herein, these discussions should not be taken as an admission that the discussed publications (for example, technical/scientific publications) are prior art for patent law purposes. For example, some or all of the discussed publications may not be sufficiently early in time, may not reflect subject matter developed early enough in time and/or may not be sufficiently enabling so as to amount to prior art for patent law purposes. To the extent that specific publications are discussed above in this Description of the Related Art Section, or elsewhere herein, they are all hereby incorporated by reference into this document in their respective entirety(ies).